• Here you can find the code and the results of all additional analyses.
  • To see the results, I recommend you directly go down to the Figures section.
  • But if you’re interested in how the analyses were executed, just tag along.
  • To see the code, click on button “Code”. Alternatively, you can download the rmd file from the github repo.
  • To execute the code, first download the actual data from AUSSDA, then run analysis.rmd, then this file

Set-up

Load packages.

# install packages
# devtools::install_github("https://github.com/tdienlin/td@v.0.0.2.5")

# define packages
packages <- c("broom.mixed", "brms", "devtools", "GGally", "ggplot2", 
              "gridExtra", "kableExtra", "knitr", "lavaan", "lme4", 
              "magrittr", "mice", "mvnormalTest", 
              "PerFit", "psych", "quanteda.textstats", "semTools", "tidyverse")

# load packages
lapply(c(packages, "td"), library, character.only = TRUE)

# load workspace
load("data/workspace_1.RData")

Results with mean scores

Instead of using factor scores, in what follows I report the results with mean scores of affect. Note that the results for life satisfaction were the same, as it was measured with a single item.

Positive affect

model_aff_pos_lmer_m <- lmerTest::lmer(aff_pos_m ~ 
                                        (1 | id) + (1 | wave) + 
                                        soc_med_read_w + soc_med_like_share_w + soc_med_post_w + 
                                        soc_med_fb_w + soc_med_ig_w + soc_med_wa_w + soc_med_yt_w + soc_med_tw_w +
                                        soc_med_read_b + soc_med_like_share_b + soc_med_post_b + 
                                        soc_med_fb_b + soc_med_ig_b + soc_med_wa_b + soc_med_yt_b + soc_med_tw_b +  
                                        age + male + born_aus + born_aus_prnts + edu_fac + employment_fac + health_b + 
                                        res_vienna + acc_bal + acc_gar + home_sqm + 
                                        med_txt_kro_b + med_txt_sta_b + med_txt_pre_b + med_txt_oes_b + med_txt_kur_b + med_txt_slz_b + med_txt_son_b + 
                                        med_vid_orf_b + med_vid_pri_b + 
                                        risk_prop_b + loc_cntrl_int_m_b + 
                                        act_wrk_w + act_spo_w + act_frn_w + act_sho_w + act_pet_w + 
                                        act_wrk_b + act_spo_b + act_frn_b + act_sho_b + act_pet_b +
                                        sat_dem_w + sat_dem_b 
                                      , data = d_long_imp)
summary(model_aff_pos_lmer_m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: aff_pos_m ~ (1 | id) + (1 | wave) + soc_med_read_w + soc_med_like_share_w +      soc_med_post_w + soc_med_fb_w + soc_med_ig_w + soc_med_wa_w +  
##     soc_med_yt_w + soc_med_tw_w + soc_med_read_b + soc_med_like_share_b +      soc_med_post_b + soc_med_fb_b + soc_med_ig_b + soc_med_wa_b +  
##     soc_med_yt_b + soc_med_tw_b + age + male + born_aus + born_aus_prnts +      edu_fac + employment_fac + health_b + res_vienna + acc_bal +  
##     acc_gar + home_sqm + med_txt_kro_b + med_txt_sta_b + med_txt_pre_b +      med_txt_oes_b + med_txt_kur_b + med_txt_slz_b + med_txt_son_b +  
##     med_vid_orf_b + med_vid_pri_b + risk_prop_b + loc_cntrl_int_m_b +      act_wrk_w + act_spo_w + act_frn_w + act_sho_w + act_pet_w +  
##     act_wrk_b + act_spo_b + act_frn_b + act_sho_b + act_pet_b +      sat_dem_w + sat_dem_b
##    Data: d_long_imp
## 
## REML criterion at convergence: 17621
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
## -4.698 -0.541 -0.012  0.559  4.922 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  id       (Intercept) 0.37922  0.6158  
##  wave     (Intercept) 0.00355  0.0596  
##  Residual             0.36413  0.6034  
## Number of obs: 7545, groups:  id, 2750; wave, 21
## 
## Fixed effects:
##                                 Estimate  Std. Error          df t value             Pr(>|t|)    
## (Intercept)                    -1.310875    0.251544 2604.846732   -5.21      0.0000002021877 ***
## soc_med_read_w                 -0.015135    0.009484 5004.643428   -1.60              0.11058    
## soc_med_like_share_w           -0.004320    0.011712 5003.039860   -0.37              0.71228    
## soc_med_post_w                 -0.008780    0.015611 5016.815188   -0.56              0.57384    
## soc_med_fb_w                    0.009831    0.010666 5013.582738    0.92              0.35672    
## soc_med_ig_w                    0.016437    0.012231 5028.416462    1.34              0.17906    
## soc_med_wa_w                    0.000464    0.008781 5019.644518    0.05              0.95787    
## soc_med_yt_w                    0.009408    0.011705 5004.636587    0.80              0.42157    
## soc_med_tw_w                   -0.008645    0.018174 5050.410047   -0.48              0.63430    
## soc_med_read_b                 -0.037684    0.017594 2968.831780   -2.14              0.03229 *  
## soc_med_like_share_b            0.007488    0.020336 3067.725050    0.37              0.71274    
## soc_med_post_b                  0.057683    0.026316 3120.881488    2.19              0.02845 *  
## soc_med_fb_b                   -0.012974    0.013029 2873.299902   -1.00              0.31942    
## soc_med_ig_b                   -0.027823    0.014930 2936.246069   -1.86              0.06249 .  
## soc_med_wa_b                    0.007310    0.013468 2801.797582    0.54              0.58733    
## soc_med_yt_b                    0.007868    0.017029 2873.460034    0.46              0.64411    
## soc_med_tw_b                    0.018375    0.021660 2824.703347    0.85              0.39630    
## age                             0.002854    0.001604 2836.943054    1.78              0.07522 .  
## male                            0.154875    0.032007 2703.989161    4.84      0.0000013792521 ***
## born_aus                       -0.017186    0.057112 2887.040351   -0.30              0.76349    
## born_aus_prnts                 -0.027299    0.019384 2756.995699   -1.41              0.15915    
## edu_facMiddle school           -0.290553    0.184059 2477.814826   -1.58              0.11456    
## edu_facVocational school       -0.326129    0.183264 2452.918621   -1.78              0.07527 .  
## edu_facTechnical school        -0.282785    0.179163 2453.945049   -1.58              0.11461    
## edu_facHigh school             -0.289496    0.184445 2480.263606   -1.57              0.11665    
## edu_facApplied high school     -0.324516    0.182698 2474.226005   -1.78              0.07582 .  
## edu_facState college           -0.126949    0.199501 2516.906853   -0.64              0.52462    
## edu_facBachelor                -0.254345    0.192822 2493.059709   -1.32              0.18727    
## edu_facMaster                  -0.384499    0.187154 2461.043400   -2.05              0.04004 *  
## edu_facPhD                     -0.331084    0.229315 2454.009061   -1.44              0.14892    
## employment_facIndustrie        -0.070816    0.073502 2765.319884   -0.96              0.33540    
## employment_facPublic service   -0.058155    0.078978 2748.289036   -0.74              0.46158    
## employment_facSelf-employed    -0.128513    0.091187 2686.626423   -1.41              0.15885    
## employment_facRetired           0.082488    0.080640 2691.088047    1.02              0.30644    
## employment_facHousekeeping      0.215052    0.124031 2690.310994    1.73              0.08306 .  
## employment_facStudent          -0.052471    0.085610 2939.931575   -0.61              0.53998    
## employment_facIncapacitated     0.087683    0.142087 3648.951069    0.62              0.53720    
## employment_facParental Leave    0.119303    0.119532 2926.053280    1.00              0.31832    
## health_b                        0.299181    0.024778 2942.891026   12.07 < 0.0000000000000002 ***
## res_vienna                     -0.024087    0.038959 2683.541720   -0.62              0.53646    
## acc_bal                        -0.049630    0.031259 2669.002999   -1.59              0.11247    
## acc_gar                        -0.021689    0.037537 2697.200706   -0.58              0.56345    
## home_sqm                        0.022525    0.007467 2857.313667    3.02              0.00258 ** 
## med_txt_kro_b                  -0.012463    0.013072 2606.876472   -0.95              0.34047    
## med_txt_sta_b                  -0.024882    0.016616 2766.255105   -1.50              0.13438    
## med_txt_pre_b                   0.005026    0.022001 2841.917866    0.23              0.81931    
## med_txt_oes_b                  -0.002163    0.016140 2720.334871   -0.13              0.89343    
## med_txt_kur_b                   0.035786    0.018135 2674.046132    1.97              0.04857 *  
## med_txt_slz_b                   0.036051    0.022636 2796.011420    1.59              0.11136    
## med_txt_son_b                   0.036798    0.012814 2717.539164    2.87              0.00412 ** 
## med_vid_orf_b                  -0.029104    0.014394 2757.485961   -2.02              0.04328 *  
## med_vid_pri_b                   0.011713    0.014337 2697.050038    0.82              0.41402    
## risk_prop_b                    -0.003421    0.009350 2932.064317   -0.37              0.71452    
## loc_cntrl_int_m_b               0.791763    0.040014 2874.855777   19.79 < 0.0000000000000002 ***
## act_wrk_w                       0.014695    0.009350 5653.283573    1.57              0.11609    
## act_spo_w                       0.078485    0.011479 5805.316283    6.84      0.0000000000089 ***
## act_frn_w                       0.037490    0.014415 3663.559812    2.60              0.00934 ** 
## act_sho_w                       0.063598    0.014363 5814.912020    4.43      0.0000096960931 ***
## act_pet_w                       0.031485    0.014009 5957.299893    2.25              0.02465 *  
## act_wrk_b                       0.039490    0.015056 2927.949480    2.62              0.00876 ** 
## act_spo_b                       0.168063    0.017547 2791.244267    9.58 < 0.0000000000000002 ***
## act_frn_b                       0.112962    0.027471 3056.131781    4.11      0.0000402470150 ***
## act_sho_b                       0.030528    0.025846 2833.832172    1.18              0.23765    
## act_pet_b                       0.022649    0.011769 2620.039346    1.92              0.05441 .  
## sat_dem_w                       0.043579    0.012119 4833.264335    3.60              0.00033 ***
## sat_dem_b                       0.050877    0.016788 2690.644339    3.03              0.00246 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

The results differed only slightly, and all inferences remained the same.

Negative Affect

model_aff_neg_lmer_m <- lmerTest::lmer(aff_neg_m ~ 
                                        (1 | id) + (1 | wave) + 
                                        soc_med_read_w + soc_med_like_share_w + soc_med_post_w + 
                                        soc_med_fb_w + soc_med_ig_w + soc_med_wa_w + soc_med_yt_w + soc_med_tw_w +
                                        soc_med_read_b + soc_med_like_share_b + soc_med_post_b + 
                                        soc_med_fb_b + soc_med_ig_b + soc_med_wa_b + soc_med_yt_b + soc_med_tw_b +  
                                        age + male + born_aus + born_aus_prnts + edu_fac + employment_fac + health_b + 
                                        res_vienna + acc_bal + acc_gar + home_sqm + 
                                        med_txt_kro_b + med_txt_sta_b + med_txt_pre_b + med_txt_oes_b + med_txt_kur_b + med_txt_slz_b + med_txt_son_b + 
                                        med_vid_orf_b + med_vid_pri_b + 
                                        risk_prop_b + loc_cntrl_int_m_b + 
                                        act_wrk_w + act_spo_w + act_frn_w + act_sho_w + act_pet_w + 
                                        act_wrk_b + act_spo_b + act_frn_b + act_sho_b + act_pet_b +
                                        sat_dem_w + sat_dem_b 
                                      , data = d_long_imp)
summary(model_aff_neg_lmer_m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: aff_neg_m ~ (1 | id) + (1 | wave) + soc_med_read_w + soc_med_like_share_w +      soc_med_post_w + soc_med_fb_w + soc_med_ig_w + soc_med_wa_w +  
##     soc_med_yt_w + soc_med_tw_w + soc_med_read_b + soc_med_like_share_b +      soc_med_post_b + soc_med_fb_b + soc_med_ig_b + soc_med_wa_b +  
##     soc_med_yt_b + soc_med_tw_b + age + male + born_aus + born_aus_prnts +      edu_fac + employment_fac + health_b + res_vienna + acc_bal +  
##     acc_gar + home_sqm + med_txt_kro_b + med_txt_sta_b + med_txt_pre_b +      med_txt_oes_b + med_txt_kur_b + med_txt_slz_b + med_txt_son_b +  
##     med_vid_orf_b + med_vid_pri_b + risk_prop_b + loc_cntrl_int_m_b +      act_wrk_w + act_spo_w + act_frn_w + act_sho_w + act_pet_w +  
##     act_wrk_b + act_spo_b + act_frn_b + act_sho_b + act_pet_b +      sat_dem_w + sat_dem_b
##    Data: d_long_imp
## 
## REML criterion at convergence: 12396
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
## -3.915 -0.449 -0.088  0.332  5.756 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  id       (Intercept) 0.1836   0.429   
##  wave     (Intercept) 0.0116   0.107   
##  Residual             0.1821   0.427   
## Number of obs: 7545, groups:  id, 2750; wave, 21
## 
## Fixed effects:
##                                Estimate Std. Error         df t value             Pr(>|t|)    
## (Intercept)                     5.07793    0.17750 2301.04855   28.61 < 0.0000000000000002 ***
## soc_med_read_w                  0.00351    0.00672 4923.73944    0.52              0.60107    
## soc_med_like_share_w            0.00674    0.00828 4881.33578    0.81              0.41604    
## soc_med_post_w                  0.01112    0.01104 4897.85401    1.01              0.31399    
## soc_med_fb_w                   -0.00115    0.00754 4891.66188   -0.15              0.87866    
## soc_med_ig_w                   -0.01445    0.00865 4905.52223   -1.67              0.09490 .  
## soc_med_wa_w                   -0.00419    0.00621 4898.62623   -0.67              0.50016    
## soc_med_yt_w                    0.01048    0.00828 4881.55926    1.27              0.20584    
## soc_med_tw_w                    0.01526    0.01285 4935.58819    1.19              0.23530    
## soc_med_read_b                 -0.00416    0.01232 2840.17601   -0.34              0.73581    
## soc_med_like_share_b            0.01313    0.01423 2939.94266    0.92              0.35642    
## soc_med_post_b                  0.07660    0.01843 2994.76154    4.16   0.0000333124893171 ***
## soc_med_fb_b                   -0.00230    0.00912 2747.10401   -0.25              0.80102    
## soc_med_ig_b                    0.01004    0.01046 2810.71988    0.96              0.33690    
## soc_med_wa_b                    0.01393    0.00942 2672.65680    1.48              0.13935    
## soc_med_yt_b                    0.00440    0.01192 2745.66423    0.37              0.71202    
## soc_med_tw_b                    0.00710    0.01515 2695.56146    0.47              0.63948    
## age                            -0.00877    0.00112 2711.74400   -7.81   0.0000000000000081 ***
## male                           -0.11409    0.02239 2574.09995   -5.10   0.0000003732894627 ***
## born_aus                       -0.10005    0.03996 2753.64818   -2.50              0.01235 *  
## born_aus_prnts                 -0.01705    0.01356 2626.54957   -1.26              0.20889    
## edu_facMiddle school           -0.08417    0.12869 2350.58322   -0.65              0.51315    
## edu_facVocational school       -0.11221    0.12813 2326.01147   -0.88              0.38127    
## edu_facTechnical school        -0.15717    0.12526 2327.19243   -1.25              0.20971    
## edu_facHigh school             -0.07636    0.12900 2353.35808   -0.59              0.55396    
## edu_facApplied high school     -0.11181    0.12775 2347.38820   -0.88              0.38154    
## edu_facState college           -0.21410    0.13953 2388.17711   -1.53              0.12506    
## edu_facBachelor                -0.16742    0.13484 2366.22509   -1.24              0.21448    
## edu_facMaster                  -0.09247    0.13086 2334.43723   -0.71              0.47989    
## edu_facPhD                     -0.01198    0.16033 2327.54123   -0.07              0.94045    
## employment_facIndustrie        -0.08509    0.05142 2634.80900   -1.65              0.09807 .  
## employment_facPublic service   -0.05380    0.05525 2618.26400   -0.97              0.33023    
## employment_facSelf-employed     0.01783    0.06377 2557.21349    0.28              0.77981    
## employment_facRetired          -0.01083    0.05640 2558.84923   -0.19              0.84770    
## employment_facHousekeeping     -0.21545    0.08676 2561.63862   -2.48              0.01308 *  
## employment_facStudent          -0.05382    0.05992 2809.92466   -0.90              0.36921    
## employment_facIncapacitated    -0.22626    0.09959 3503.67580   -2.27              0.02316 *  
## employment_facParental Leave   -0.08258    0.08365 2787.47201   -0.99              0.32364    
## health_b                       -0.27833    0.01736 2812.40379  -16.03 < 0.0000000000000002 ***
## res_vienna                     -0.05931    0.02729 2558.88260   -2.17              0.02986 *  
## acc_bal                        -0.03910    0.02187 2541.58874   -1.79              0.07395 .  
## acc_gar                        -0.05545    0.02626 2566.85378   -2.11              0.03485 *  
## home_sqm                       -0.01897    0.00523 2724.69277   -3.63              0.00029 ***
## med_txt_kro_b                  -0.00959    0.00914 2480.56422   -1.05              0.29431    
## med_txt_sta_b                   0.03244    0.01163 2637.87644    2.79              0.00531 ** 
## med_txt_pre_b                   0.00473    0.01539 2711.35005    0.31              0.75845    
## med_txt_oes_b                   0.00523    0.01129 2592.62030    0.46              0.64336    
## med_txt_kur_b                  -0.00203    0.01269 2545.83863   -0.16              0.87307    
## med_txt_slz_b                   0.00438    0.01584 2668.40324    0.28              0.78195    
## med_txt_son_b                  -0.01095    0.00896 2588.11129   -1.22              0.22201    
## med_vid_orf_b                   0.02610    0.01007 2629.96616    2.59              0.00963 ** 
## med_vid_pri_b                  -0.00181    0.01003 2568.30866   -0.18              0.85671    
## risk_prop_b                    -0.01858    0.00655 2803.28222   -2.84              0.00457 ** 
## loc_cntrl_int_m_b              -0.54050    0.02800 2744.48083  -19.30 < 0.0000000000000002 ***
## act_wrk_w                      -0.00190    0.00663 5708.15111   -0.29              0.77426    
## act_spo_w                       0.01307    0.00812 5732.61914    1.61              0.10754    
## act_frn_w                       0.02799    0.01032 5728.52597    2.71              0.00670 ** 
## act_sho_w                       0.00657    0.01016 5739.28499    0.65              0.51783    
## act_pet_w                       0.06595    0.00990 5886.48542    6.66   0.0000000000294937 ***
## act_wrk_b                       0.00633    0.01055 2807.23714    0.60              0.54862    
## act_spo_b                       0.02656    0.01227 2661.98521    2.16              0.03054 *  
## act_frn_b                       0.08489    0.01931 2984.15915    4.40   0.0000114027144649 ***
## act_sho_b                       0.02314    0.01808 2705.21660    1.28              0.20070    
## act_pet_b                       0.01442    0.00823 2492.71236    1.75              0.07994 .  
## sat_dem_w                      -0.03616    0.00862 5760.26056   -4.20   0.0000276712616522 ***
## sat_dem_b                      -0.05810    0.01174 2563.13126   -4.95   0.0000007983167606 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

The results differed only slightly, and all inferences remained the same.

Results without imputed data

We first need to export factor scores for variables without imputed data

model <- "
aff_pos =~ a1*aff_pos_1 + a2*aff_pos_2 + a3*aff_pos_3
"
cfa_aff_pos <- cfa(model, d_long, group = "wave", estimator = "MLM")
d_long$aff_pos_fs <- get_fs(cfa_aff_pos)

model <- "
aff_neg =~ a1*aff_neg_1 + a2*aff_neg_2 + a3*aff_neg_3 + a4*aff_neg_4 + a5*aff_neg_5 + a6*aff_neg_6
"
cfa_aff_neg <- cfa(model, d_long, group = "wave", estimator = "MLM")
d_long$aff_neg_fs <- get_fs(cfa_aff_neg)

Life satisfaction

model_life_sat_lmer_noi <- lmerTest::lmer(life_sat ~
                                        (1 | id) + (1 | wave) + 
                                        soc_med_read_w + soc_med_like_share_w + soc_med_post_w  + 
                                        soc_med_fb_w + soc_med_ig_w + soc_med_wa_w + soc_med_yt_w + soc_med_tw_w +
                                        soc_med_read_b + soc_med_like_share_b + soc_med_post_b + 
                                        soc_med_fb_b + soc_med_ig_b + soc_med_wa_b + soc_med_yt_b + soc_med_tw_b +  
                                        age + male + born_aus + born_aus_prnts + edu_fac + employment_fac + health_b + 
                                        res_vienna + acc_bal + acc_gar + home_sqm + 
                                        med_txt_kro_b + med_txt_sta_b + med_txt_pre_b + med_txt_oes_b + med_txt_kur_b + med_txt_slz_b + med_txt_son_b + 
                                        med_vid_orf_b + med_vid_pri_b + 
                                        risk_prop_b + loc_cntrl_int_m_b + 
                                        act_wrk_w + act_spo_w + act_frn_w + act_sho_w + act_pet_w + 
                                        act_wrk_b + act_spo_b + act_frn_b + act_sho_b + act_pet_b +
                                        sat_dem_w + sat_dem_b 
                                      , data = d_long)
summary(model_life_sat_lmer_noi)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: life_sat ~ (1 | id) + (1 | wave) + soc_med_read_w + soc_med_like_share_w +      soc_med_post_w + soc_med_fb_w + soc_med_ig_w + soc_med_wa_w +  
##     soc_med_yt_w + soc_med_tw_w + soc_med_read_b + soc_med_like_share_b +      soc_med_post_b + soc_med_fb_b + soc_med_ig_b + soc_med_wa_b +  
##     soc_med_yt_b + soc_med_tw_b + age + male + born_aus + born_aus_prnts +      edu_fac + employment_fac + health_b + res_vienna + acc_bal +  
##     acc_gar + home_sqm + med_txt_kro_b + med_txt_sta_b + med_txt_pre_b +      med_txt_oes_b + med_txt_kur_b + med_txt_slz_b + med_txt_son_b +  
##     med_vid_orf_b + med_vid_pri_b + risk_prop_b + loc_cntrl_int_m_b +      act_wrk_w + act_spo_w + act_frn_w + act_sho_w + act_pet_w +  
##     act_wrk_b + act_spo_b + act_frn_b + act_sho_b + act_pet_b +      sat_dem_w + sat_dem_b
##    Data: d_long
## 
## REML criterion at convergence: 29235
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
## -4.426 -0.365  0.136  0.518  4.243 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  id       (Intercept) 1.4930   1.222   
##  wave     (Intercept) 0.0135   0.116   
##  Residual             3.1138   1.765   
## Number of obs: 6810, groups:  id, 2446; wave, 21
## 
## Fixed effects:
##                                Estimate Std. Error         df t value             Pr(>|t|)    
## (Intercept)                    -2.51071    0.61482 2155.57944   -4.08            0.0000460 ***
## soc_med_read_w                  0.03828    0.02891 4519.41534    1.32              0.18554    
## soc_med_like_share_w            0.00956    0.03580 4667.08062    0.27              0.78951    
## soc_med_post_w                 -0.08038    0.04855 4726.16954   -1.66              0.09784 .  
## soc_med_fb_w                   -0.04051    0.03286 4707.13494   -1.23              0.21774    
## soc_med_ig_w                    0.07679    0.03757 4726.03036    2.04              0.04100 *  
## soc_med_wa_w                   -0.01410    0.02697 4702.45392   -0.52              0.60107    
## soc_med_yt_w                    0.05883    0.03597 4683.62478    1.64              0.10194    
## soc_med_tw_w                   -0.15357    0.05685 4789.56076   -2.70              0.00693 ** 
## soc_med_read_b                 -0.01446    0.04380 2584.74272   -0.33              0.74135    
## soc_med_like_share_b           -0.04865    0.05120 2669.77730   -0.95              0.34211    
## soc_med_post_b                 -0.00712    0.06733 2819.32135   -0.11              0.91574    
## soc_med_fb_b                    0.00504    0.03212 2484.82200    0.16              0.87522    
## soc_med_ig_b                    0.00570    0.03719 2545.27684    0.15              0.87816    
## soc_med_wa_b                   -0.02308    0.03288 2360.26812   -0.70              0.48287    
## soc_med_yt_b                    0.02850    0.04184 2476.92539    0.68              0.49589    
## soc_med_tw_b                   -0.01208    0.05300 2382.76737   -0.23              0.81969    
## age                             0.00843    0.00392 2296.30904    2.15              0.03157 *  
## male                            0.16245    0.07580 2209.56339    2.14              0.03221 *  
## born_aus                       -0.26115    0.14431 2428.67854   -1.81              0.07047 .  
## born_aus_prnts                 -0.00168    0.04761 2258.05747   -0.04              0.97190    
## edu_facMiddle school           -0.69129    0.46349 2046.08561   -1.49              0.13599    
## edu_facVocational school       -0.62496    0.45982 2024.92885   -1.36              0.17426    
## edu_facTechnical school        -0.77048    0.45101 2023.30303   -1.71              0.08773 .  
## edu_facHigh school             -0.74779    0.46399 2049.96949   -1.61              0.10719    
## edu_facApplied high school     -0.73710    0.45943 2043.51212   -1.60              0.10878    
## edu_facState college           -0.98202    0.49867 2090.63365   -1.97              0.04905 *  
## edu_facBachelor                -0.42734    0.48410 2067.26523   -0.88              0.37748    
## edu_facMaster                  -0.68408    0.46826 2036.67919   -1.46              0.14420    
## edu_facPhD                     -0.31172    0.56822 2022.95592   -0.55              0.58334    
## employment_facIndustrie         0.59702    0.18155 2293.18300    3.29              0.00102 ** 
## employment_facPublic service    0.66382    0.19352 2298.69000    3.43              0.00061 ***
## employment_facSelf-employed     0.32794    0.22192 2243.04266    1.48              0.13962    
## employment_facRetired           0.60074    0.19558 2168.99492    3.07              0.00216 ** 
## employment_facHousekeeping      0.96023    0.30917 2227.64770    3.11              0.00192 ** 
## employment_facStudent           0.68693    0.21624 2449.90313    3.18              0.00151 ** 
## employment_facIncapacitated     0.91453    0.37902 2559.66865    2.41              0.01590 *  
## employment_facParental Leave    1.22302    0.29776 2302.47827    4.11            0.0000414 ***
## health_b                        0.69729    0.05657 2364.74643   12.33 < 0.0000000000000002 ***
## res_vienna                      0.09212    0.09429 2202.71127    0.98              0.32870    
## acc_bal                         0.04812    0.07567 2224.58087    0.64              0.52487    
## acc_gar                         0.08216    0.09149 2209.08739    0.90              0.36925    
## home_sqm                        0.05242    0.01828 2340.40839    2.87              0.00418 ** 
## med_txt_kro_b                   0.06672    0.03084 2191.97905    2.16              0.03061 *  
## med_txt_sta_b                   0.04107    0.04012 2306.25672    1.02              0.30610    
## med_txt_pre_b                   0.04071    0.05271 2448.87757    0.77              0.43992    
## med_txt_oes_b                  -0.02597    0.03926 2288.25793   -0.66              0.50845    
## med_txt_kur_b                   0.02829    0.04353 2197.34601    0.65              0.51578    
## med_txt_slz_b                   0.00119    0.05608 2344.39766    0.02              0.98310    
## med_txt_son_b                  -0.01577    0.03044 2262.91458   -0.52              0.60446    
## med_vid_orf_b                  -0.01314    0.03472 2328.33653   -0.38              0.70522    
## med_vid_pri_b                   0.03082    0.03450 2265.50587    0.89              0.37173    
## risk_prop_b                     0.02607    0.01811 2296.76400    1.44              0.15022    
## loc_cntrl_int_m_b               1.24674    0.07841 2246.41797   15.90 < 0.0000000000000002 ***
## act_wrk_w                       0.08063    0.02809 5010.63416    2.87              0.00411 ** 
## act_spo_w                       0.02403    0.03453 5730.52007    0.70              0.48647    
## act_frn_w                       0.03800    0.04313 1446.26720    0.88              0.37840    
## act_sho_w                       0.07859    0.04326 5664.02146    1.82              0.06933 .  
## act_pet_w                      -0.05268    0.04482 5871.78783   -1.18              0.23986    
## act_wrk_b                       0.04303    0.03715 2429.52572    1.16              0.24685    
## act_spo_b                       0.08478    0.04301 2359.01876    1.97              0.04883 *  
## act_frn_b                       0.14628    0.06929 2640.36336    2.11              0.03487 *  
## act_sho_b                      -0.04772    0.06417 2423.69359   -0.74              0.45717    
## act_pet_b                       0.02584    0.02802 2215.49777    0.92              0.35656    
## sat_dem_w                       0.17715    0.03653 2553.09600    4.85            0.0000013 ***
## sat_dem_b                       0.37012    0.04037 2255.14438    9.17 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Positive Affect

model_aff_pos_lmer_noi <- lmerTest::lmer(aff_pos_fs ~ 
                                        (1 | id) + (1 | wave) + 
                                        soc_med_read_w + soc_med_like_share_w + soc_med_post_w + 
                                        soc_med_fb_w + soc_med_ig_w + soc_med_wa_w + soc_med_yt_w + soc_med_tw_w +
                                        soc_med_read_b + soc_med_like_share_b + soc_med_post_b + 
                                        soc_med_fb_b + soc_med_ig_b + soc_med_wa_b + soc_med_yt_b + soc_med_tw_b +  
                                        age + male + born_aus + born_aus_prnts + edu_fac + employment_fac + health_b + 
                                        res_vienna + acc_bal + acc_gar + home_sqm + 
                                        med_txt_kro_b + med_txt_sta_b + med_txt_pre_b + med_txt_oes_b + med_txt_kur_b + med_txt_slz_b + med_txt_son_b + 
                                        med_vid_orf_b + med_vid_pri_b + 
                                        risk_prop_b + loc_cntrl_int_m_b + 
                                        act_wrk_w + act_spo_w + act_frn_w + act_sho_w + act_pet_w + 
                                        act_wrk_b + act_spo_b + act_frn_b + act_sho_b + act_pet_b +
                                        sat_dem_w + sat_dem_b 
                                      , data = d_long)
summary(model_aff_pos_lmer_noi)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: aff_pos_fs ~ (1 | id) + (1 | wave) + soc_med_read_w + soc_med_like_share_w +      soc_med_post_w + soc_med_fb_w + soc_med_ig_w + soc_med_wa_w +  
##     soc_med_yt_w + soc_med_tw_w + soc_med_read_b + soc_med_like_share_b +      soc_med_post_b + soc_med_fb_b + soc_med_ig_b + soc_med_wa_b +  
##     soc_med_yt_b + soc_med_tw_b + age + male + born_aus + born_aus_prnts +      edu_fac + employment_fac + health_b + res_vienna + acc_bal +  
##     acc_gar + home_sqm + med_txt_kro_b + med_txt_sta_b + med_txt_pre_b +      med_txt_oes_b + med_txt_kur_b + med_txt_slz_b + med_txt_son_b +  
##     med_vid_orf_b + med_vid_pri_b + risk_prop_b + loc_cntrl_int_m_b +      act_wrk_w + act_spo_w + act_frn_w + act_sho_w + act_pet_w +  
##     act_wrk_b + act_spo_b + act_frn_b + act_sho_b + act_pet_b +      sat_dem_w + sat_dem_b
##    Data: d_long
## 
## REML criterion at convergence: 12993
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
## -4.763 -0.536 -0.020  0.546  3.640 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  id       (Intercept) 0.29589  0.5440  
##  wave     (Intercept) 0.00273  0.0523  
##  Residual             0.23998  0.4899  
## Number of obs: 6585, groups:  id, 2409; wave, 21
## 
## Fixed effects:
##                                 Estimate  Std. Error          df t value             Pr(>|t|)    
## (Intercept)                    -0.251453    0.235466 2322.840602   -1.07              0.28568    
## soc_med_read_w                 -0.007765    0.008273 4405.881629   -0.94              0.34798    
## soc_med_like_share_w           -0.007239    0.010217 4398.376663   -0.71              0.47870    
## soc_med_post_w                  0.001665    0.014015 4422.922246    0.12              0.90542    
## soc_med_fb_w                    0.011608    0.009346 4388.639077    1.24              0.21430    
## soc_med_ig_w                    0.009042    0.010726 4417.926222    0.84              0.39930    
## soc_med_wa_w                   -0.005624    0.007707 4396.628465   -0.73              0.46562    
## soc_med_yt_w                    0.010549    0.010299 4396.126977    1.02              0.30576    
## soc_med_tw_w                   -0.006638    0.016483 4462.858202   -0.40              0.68716    
## soc_med_read_b                 -0.019866    0.016525 2543.290576   -1.20              0.22939    
## soc_med_like_share_b            0.000221    0.019244 2598.354480    0.01              0.99086    
## soc_med_post_b                  0.026117    0.025011 2687.454459    1.04              0.29649    
## soc_med_fb_b                   -0.015698    0.012156 2478.792607   -1.29              0.19668    
## soc_med_ig_b                   -0.013138    0.014009 2504.985338   -0.94              0.34842    
## soc_med_wa_b                    0.012450    0.012559 2400.016679    0.99              0.32162    
## soc_med_yt_b                   -0.007925    0.015847 2466.003453   -0.50              0.61705    
## soc_med_tw_b                    0.009078    0.020045 2425.618906    0.45              0.65066    
## age                             0.002983    0.001495 2358.836249    1.99              0.04617 *  
## male                            0.129437    0.029063 2307.057609    4.45         0.0000088453 ***
## born_aus                       -0.035881    0.055035 2435.378352   -0.65              0.51449    
## born_aus_prnts                 -0.029380    0.018222 2323.495883   -1.61              0.10703    
## edu_facMiddle school           -0.216029    0.177658 2283.302625   -1.22              0.22412    
## edu_facVocational school       -0.248019    0.176582 2268.054139   -1.40              0.16029    
## edu_facTechnical school        -0.216730    0.173037 2270.659571   -1.25              0.21051    
## edu_facHigh school             -0.231175    0.177914 2285.081227   -1.30              0.19395    
## edu_facApplied high school     -0.260034    0.176162 2282.148289   -1.48              0.14005    
## edu_facState college           -0.127211    0.191118 2304.489755   -0.67              0.50572    
## edu_facBachelor                -0.180970    0.185434 2289.445887   -0.98              0.32920    
## edu_facMaster                  -0.317628    0.179611 2275.332182   -1.77              0.07712 .  
## edu_facPhD                     -0.178745    0.218231 2213.682766   -0.82              0.41284    
## employment_facIndustrie         0.007239    0.069434 2350.671242    0.10              0.91698    
## employment_facPublic service    0.027403    0.073966 2352.307982    0.37              0.71106    
## employment_facSelf-employed    -0.007343    0.084993 2326.965475   -0.09              0.93116    
## employment_facRetired           0.137210    0.075490 2275.073418    1.82              0.06926 .  
## employment_facHousekeeping      0.236707    0.120789 2284.268231    1.96              0.05016 .  
## employment_facStudent          -0.014668    0.082226 2443.214066   -0.18              0.85843    
## employment_facIncapacitated     0.066985    0.140528 2565.044010    0.48              0.63364    
## employment_facParental Leave    0.252210    0.113669 2354.902108    2.22              0.02659 *  
## health_b                        0.287513    0.021437 2427.973675   13.41 < 0.0000000000000002 ***
## res_vienna                     -0.025200    0.036206 2306.564930   -0.70              0.48648    
## acc_bal                        -0.032255    0.029023 2310.950523   -1.11              0.26654    
## acc_gar                        -0.003219    0.035033 2299.183581   -0.09              0.92679    
## home_sqm                        0.020475    0.006965 2376.277899    2.94              0.00332 ** 
## med_txt_kro_b                  -0.006776    0.011837 2301.030302   -0.57              0.56708    
## med_txt_sta_b                  -0.014156    0.015297 2358.999342   -0.93              0.35482    
## med_txt_pre_b                   0.016354    0.019936 2469.560879    0.82              0.41212    
## med_txt_oes_b                  -0.011649    0.014988 2368.055099   -0.78              0.43710    
## med_txt_kur_b                   0.027678    0.016680 2302.796452    1.66              0.09718 .  
## med_txt_slz_b                   0.023080    0.021378 2391.067901    1.08              0.28043    
## med_txt_son_b                   0.025612    0.011613 2335.647120    2.21              0.02752 *  
## med_vid_orf_b                  -0.032531    0.013220 2387.913749   -2.46              0.01394 *  
## med_vid_pri_b                   0.018843    0.013187 2341.072898    1.43              0.15317    
## risk_prop_b                    -0.002242    0.006918 2361.740139   -0.32              0.74590    
## loc_cntrl_int_m_b               0.459008    0.029989 2325.334440   15.31 < 0.0000000000000002 ***
## act_wrk_w                       0.021637    0.008230 4863.306966    2.63              0.00859 ** 
## act_spo_w                       0.065440    0.010101 5034.723499    6.48         0.0000000001 ***
## act_frn_w                       0.033281    0.012895 3220.738930    2.58              0.00990 ** 
## act_sho_w                       0.049496    0.012746 5030.721608    3.88              0.00010 ***
## act_pet_w                       0.024755    0.013389 5204.162349    1.85              0.06454 .  
## act_wrk_b                       0.031708    0.014109 2451.363866    2.25              0.02471 *  
## act_spo_b                       0.139191    0.016400 2400.510244    8.49 < 0.0000000000000002 ***
## act_frn_b                       0.097353    0.025973 2642.422600    3.75              0.00018 ***
## act_sho_b                       0.032657    0.024408 2451.554298    1.34              0.18104    
## act_pet_b                       0.013775    0.010753 2302.982895    1.28              0.20031    
## sat_dem_w                       0.037327    0.010778 4216.627972    3.46              0.00054 ***
## sat_dem_b                       0.052760    0.015395 2346.241860    3.43              0.00062 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Negative Affect

model_aff_neg_lmer_noi <- lmerTest::lmer(aff_neg_fs ~ 
                                        (1 | id) + (1 | wave) + 
                                        soc_med_read_w + soc_med_like_share_w + soc_med_post_w + 
                                        soc_med_fb_w + soc_med_ig_w + soc_med_wa_w + soc_med_yt_w + soc_med_tw_w +
                                        soc_med_read_b + soc_med_like_share_b + soc_med_post_b + 
                                        soc_med_fb_b + soc_med_ig_b + soc_med_wa_b + soc_med_yt_b + soc_med_tw_b +  
                                        age + male + born_aus + born_aus_prnts + edu_fac + employment_fac + health_b + 
                                        res_vienna + acc_bal + acc_gar + home_sqm + 
                                        med_txt_kro_b + med_txt_sta_b + med_txt_pre_b + med_txt_oes_b + med_txt_kur_b + med_txt_slz_b + med_txt_son_b + 
                                        med_vid_orf_b + med_vid_pri_b + 
                                        risk_prop_b + loc_cntrl_int_m_b + 
                                        act_wrk_w + act_spo_w + act_frn_w + act_sho_w + act_pet_w + 
                                        act_wrk_b + act_spo_b + act_frn_b + act_sho_b + act_pet_b +
                                        sat_dem_w + sat_dem_b 
                                      , data = d_long)
summary(model_aff_neg_lmer_noi)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: aff_neg_fs ~ (1 | id) + (1 | wave) + soc_med_read_w + soc_med_like_share_w +      soc_med_post_w + soc_med_fb_w + soc_med_ig_w + soc_med_wa_w +  
##     soc_med_yt_w + soc_med_tw_w + soc_med_read_b + soc_med_like_share_b +      soc_med_post_b + soc_med_fb_b + soc_med_ig_b + soc_med_wa_b +  
##     soc_med_yt_b + soc_med_tw_b + age + male + born_aus + born_aus_prnts +      edu_fac + employment_fac + health_b + res_vienna + acc_bal +  
##     acc_gar + home_sqm + med_txt_kro_b + med_txt_sta_b + med_txt_pre_b +      med_txt_oes_b + med_txt_kur_b + med_txt_slz_b + med_txt_son_b +  
##     med_vid_orf_b + med_vid_pri_b + risk_prop_b + loc_cntrl_int_m_b +      act_wrk_w + act_spo_w + act_frn_w + act_sho_w + act_pet_w +  
##     act_wrk_b + act_spo_b + act_frn_b + act_sho_b + act_pet_b +      sat_dem_w + sat_dem_b
##    Data: d_long
## 
## REML criterion at convergence: 8307
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
## -4.174 -0.417 -0.096  0.286  5.572 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  id       (Intercept) 0.13712  0.3703  
##  wave     (Intercept) 0.00817  0.0904  
##  Residual             0.12022  0.3467  
## Number of obs: 6536, groups:  id, 2389; wave, 21
## 
## Fixed effects:
##                                 Estimate  Std. Error          df t value             Pr(>|t|)    
## (Intercept)                     4.190498    0.162965 2054.226006   25.71 < 0.0000000000000002 ***
## soc_med_read_w                  0.003400    0.005909 4327.073223    0.58              0.56508    
## soc_med_like_share_w            0.004930    0.007233 4263.092378    0.68              0.49557    
## soc_med_post_w                  0.009216    0.009865 4315.697568    0.93              0.35024    
## soc_med_fb_w                   -0.004669    0.006647 4288.327697   -0.70              0.48247    
## soc_med_ig_w                   -0.006661    0.007662 4309.062182   -0.87              0.38469    
## soc_med_wa_w                   -0.000319    0.005495 4288.463591   -0.06              0.95364    
## soc_med_yt_w                    0.006785    0.007323 4275.712539    0.93              0.35418    
## soc_med_tw_w                    0.006641    0.011678 4351.395073    0.57              0.56961    
## soc_med_read_b                 -0.008590    0.011456 2419.276965   -0.75              0.45344    
## soc_med_like_share_b            0.008483    0.013419 2446.786139    0.63              0.52732    
## soc_med_post_b                  0.065517    0.017465 2558.393338    3.75              0.00018 ***
## soc_med_fb_b                    0.004350    0.008423 2360.035779    0.52              0.60557    
## soc_med_ig_b                    0.006348    0.009729 2377.048773    0.65              0.51413    
## soc_med_wa_b                    0.006314    0.008678 2279.250892    0.73              0.46696    
## soc_med_yt_b                    0.024584    0.010940 2344.029200    2.25              0.02472 *  
## soc_med_tw_b                    0.002761    0.013904 2287.593394    0.20              0.84264    
## age                            -0.006216    0.001037 2239.381657   -5.99       0.000000002397 ***
## male                           -0.133158    0.020134 2174.320800   -6.61       0.000000000047 ***
## born_aus                       -0.034599    0.037957 2304.141663   -0.91              0.36211    
## born_aus_prnts                 -0.010716    0.012611 2205.154927   -0.85              0.39556    
## edu_facMiddle school           -0.134382    0.121654 2100.699996   -1.10              0.26945    
## edu_facVocational school       -0.161957    0.120860 2084.070526   -1.34              0.18038    
## edu_facTechnical school        -0.188971    0.118354 2086.014810   -1.60              0.11049    
## edu_facHigh school             -0.104591    0.121706 2104.873890   -0.86              0.39023    
## edu_facApplied high school     -0.128530    0.120515 2100.188032   -1.07              0.28632    
## edu_facState college           -0.259390    0.130879 2128.097430   -1.98              0.04762 *  
## edu_facBachelor                -0.146052    0.126910 2111.919936   -1.15              0.24993    
## edu_facMaster                  -0.114350    0.122947 2093.679541   -0.93              0.35244    
## edu_facPhD                     -0.137904    0.149653 2059.378699   -0.92              0.35690    
## employment_facIndustrie        -0.116058    0.047991 2215.434361   -2.42              0.01567 *  
## employment_facPublic service   -0.078666    0.051129 2220.501435   -1.54              0.12405    
## employment_facSelf-employed    -0.055497    0.058739 2181.920876   -0.94              0.34486    
## employment_facRetired          -0.073571    0.051914 2142.172369   -1.42              0.15658    
## employment_facHousekeeping     -0.217237    0.082302 2203.519577   -2.64              0.00836 ** 
## employment_facStudent          -0.059296    0.056898 2312.875856   -1.04              0.29745    
## employment_facIncapacitated    -0.148682    0.099650 2441.084705   -1.49              0.13582    
## employment_facParental Leave   -0.160658    0.079192 2206.127263   -2.03              0.04261 *  
## health_b                       -0.224659    0.014942 2290.259557  -15.04 < 0.0000000000000002 ***
## res_vienna                     -0.049726    0.025057 2188.920510   -1.98              0.04732 *  
## acc_bal                        -0.031995    0.020074 2190.378868   -1.59              0.11111    
## acc_gar                        -0.038232    0.024263 2178.435301   -1.58              0.11523    
## home_sqm                       -0.016765    0.004807 2257.661976   -3.49              0.00050 ***
## med_txt_kro_b                  -0.001817    0.008166 2170.176687   -0.22              0.82395    
## med_txt_sta_b                   0.021724    0.010558 2225.789566    2.06              0.03974 *  
## med_txt_pre_b                   0.000878    0.013847 2303.765930    0.06              0.94945    
## med_txt_oes_b                   0.006126    0.010364 2234.486416    0.59              0.55451    
## med_txt_kur_b                   0.002257    0.011518 2162.822155    0.20              0.84469    
## med_txt_slz_b                   0.006760    0.014697 2272.800334    0.46              0.64562    
## med_txt_son_b                  -0.005418    0.008035 2207.800875   -0.67              0.50015    
## med_vid_orf_b                   0.023341    0.009162 2249.297356    2.55              0.01091 *  
## med_vid_pri_b                  -0.007077    0.009119 2202.742036   -0.78              0.43776    
## risk_prop_b                    -0.007821    0.004804 2228.875705   -1.63              0.10365    
## loc_cntrl_int_m_b              -0.338477    0.020752 2189.652446  -16.31 < 0.0000000000000002 ***
## act_wrk_w                      -0.003576    0.005866 4923.412715   -0.61              0.54209    
## act_spo_w                       0.001059    0.007203 4932.175328    0.15              0.88309    
## act_frn_w                       0.017008    0.009311 4860.837927    1.83              0.06782 .  
## act_sho_w                       0.005728    0.009027 4960.956414    0.63              0.52576    
## act_pet_w                       0.053163    0.009427 5064.578721    5.64       0.000000017990 ***
## act_wrk_b                       0.006986    0.009766 2340.835518    0.72              0.47447    
## act_spo_b                       0.019346    0.011359 2278.999548    1.70              0.08868 .  
## act_frn_b                       0.031532    0.018164 2529.154304    1.74              0.08269 .  
## act_sho_b                       0.023070    0.016918 2300.096745    1.36              0.17283    
## act_pet_b                       0.011633    0.007431 2173.229169    1.57              0.11763    
## sat_dem_w                      -0.023544    0.007677 4916.580727   -3.07              0.00218 ** 
## sat_dem_b                      -0.047654    0.010628 2212.329562   -4.48       0.000007713859 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Results with multiple imputation

Life satisfaction

model_life_sat_lmer_mim <- with(d_long_mim_mice, exp =  
                                lmerTest::lmer(life_sat ~
                                        (1 | id) + (1 | wave) + 
                                        soc_med_read_w + soc_med_like_share_w + soc_med_post_w  + 
                                        soc_med_fb_w + soc_med_ig_w + soc_med_wa_w + soc_med_yt_w + soc_med_tw_w +
                                        soc_med_read_b + soc_med_like_share_b + soc_med_post_b + 
                                        soc_med_fb_b + soc_med_ig_b + soc_med_wa_b + soc_med_yt_b + soc_med_tw_b +  
                                        age + male + born_aus + born_aus_prnts + edu_fac + employment_fac + health_b + 
                                        res_vienna + acc_bal + acc_gar + home_sqm + 
                                        med_txt_kro_b + med_txt_sta_b + med_txt_pre_b + med_txt_oes_b + med_txt_kur_b + med_txt_slz_b + med_txt_son_b + 
                                        med_vid_orf_b + med_vid_pri_b + 
                                        risk_prop_b + loc_cntrl_int_m_b + 
                                        act_wrk_w + act_spo_w + act_frn_w + act_sho_w + act_pet_w + 
                                        act_wrk_b + act_spo_b + act_frn_b + act_sho_b + act_pet_b +
                                        sat_dem_w + sat_dem_b 
                                      ))
model_life_sat_lmer_mim <- summary(pool(model_life_sat_lmer_mim), conf.int = TRUE)

Positive Affect

For simplicity, note that we use mean-scores and not factors scores here.

model_aff_pos_lmer_mim <- with(d_long_mim_mice, exp =  
                           lmerTest::lmer(aff_pos_m ~ 
                                        (1 | id) + (1 | wave) + 
                                        soc_med_read_w + soc_med_like_share_w + soc_med_post_w + 
                                        soc_med_fb_w + soc_med_ig_w + soc_med_wa_w + soc_med_yt_w + soc_med_tw_w +
                                        soc_med_read_b + soc_med_like_share_b + soc_med_post_b + 
                                        soc_med_fb_b + soc_med_ig_b + soc_med_wa_b + soc_med_yt_b + soc_med_tw_b +  
                                        age + male + born_aus + born_aus_prnts + edu_fac + employment_fac + health_b + 
                                        res_vienna + acc_bal + acc_gar + home_sqm + 
                                        med_txt_kro_b + med_txt_sta_b + med_txt_pre_b + med_txt_oes_b + med_txt_kur_b + med_txt_slz_b + med_txt_son_b + 
                                        med_vid_orf_b + med_vid_pri_b + 
                                        risk_prop_b + loc_cntrl_int_m_b + 
                                        act_wrk_w + act_spo_w + act_frn_w + act_sho_w + act_pet_w + 
                                        act_wrk_b + act_spo_b + act_frn_b + act_sho_b + act_pet_b +
                                        sat_dem_w + sat_dem_b)
)
model_aff_pos_lmer_mim <- summary(pool(model_aff_pos_lmer_mim), conf.int = TRUE)

Negative Affect

For simplicity, note that we use mean-scores and not factors scores here.

model_aff_neg_lmer_mim <- with(d_long_mim_mice, exp =  
                               lmerTest::lmer(aff_neg_m ~ 
                                        (1 | id) + (1 | wave) + 
                                        soc_med_read_w + soc_med_like_share_w + soc_med_post_w + 
                                        soc_med_fb_w + soc_med_ig_w + soc_med_wa_w + soc_med_yt_w + soc_med_tw_w +
                                        soc_med_read_b + soc_med_like_share_b + soc_med_post_b + 
                                        soc_med_fb_b + soc_med_ig_b + soc_med_wa_b + soc_med_yt_b + soc_med_tw_b +  
                                        age + male + born_aus + born_aus_prnts + edu_fac + employment_fac + health_b + 
                                        res_vienna + acc_bal + acc_gar + home_sqm + 
                                        med_txt_kro_b + med_txt_sta_b + med_txt_pre_b + med_txt_oes_b + med_txt_kur_b + med_txt_slz_b + med_txt_son_b + 
                                        med_vid_orf_b + med_vid_pri_b + 
                                        risk_prop_b + loc_cntrl_int_m_b + 
                                        act_wrk_w + act_spo_w + act_frn_w + act_sho_w + act_pet_w + 
                                        act_wrk_b + act_spo_b + act_frn_b + act_sho_b + act_pet_b +
                                        sat_dem_w + sat_dem_b)
)
model_aff_neg_lmer_mim <- summary(pool(model_aff_neg_lmer_mim), conf.int = TRUE)

Figures

In what follows, see figure with all results combined.

Activities

# get data
dat_fig_results_activity <- get_dat_res(model_aff_neg_lmer, model_aff_pos_lmer, model_life_sat_lmer, type = "activity", analysis = "1. regular")
dat_fig_results_activity_nco <- get_dat_res(model_aff_neg_lmer_nco, model_aff_pos_lmer_nco, model_life_sat_lmer_nco, type = "activity", analysis = "2. no covars")
dat_fig_results_activity_m <- get_dat_res(model_aff_neg_lmer_m, model_aff_pos_lmer_m, model_life_sat_lmer, type = "activity", analysis = "3. mean scores")
dat_fig_results_activity_noi <- get_dat_res(model_aff_neg_lmer_noi, model_aff_pos_lmer_noi, model_life_sat_lmer_noi, type = "activity", analysis = "4. no imp")
dat_fig_results_activity_mim <- get_dat_res(model_aff_neg_lmer_mim, model_aff_pos_lmer_mim, model_life_sat_lmer_mim, type = "activity", analysis = "5. mult imp")

dat_fig_results_activity <- rbind(
  dat_fig_results_activity,
  dat_fig_results_activity_nco,
  dat_fig_results_activity_m,
  dat_fig_results_activity_noi,
  dat_fig_results_activity_mim
)
# make figure
fig_results_activity_comparison <- make_graph_res(
  dat_fig_results_activity,
  sesoi = "est"
  )
fig_results_activity_comparison

# safe figure
ggsave("figures/fig_results_activity_comparison.pdf", 
       width = 7, height = 4,
       plot = fig_results_activity)

Channels

# get data
dat_fig_results_channels <- get_dat_res(model_aff_neg_lmer, model_aff_pos_lmer, model_life_sat_lmer, type = "channels", analysis = "1. regular")
dat_fig_results_channels_nco <- get_dat_res(model_aff_neg_lmer_nco, model_aff_pos_lmer_nco, model_life_sat_lmer_nco, type = "channels", analysis = "2. no covars")
dat_fig_results_channels_m <- get_dat_res(model_aff_neg_lmer_m, model_aff_pos_lmer_m, model_life_sat_lmer, type = "channels", analysis = "3. mean scores")
dat_fig_results_channels_noi <- get_dat_res(model_aff_neg_lmer_noi, model_aff_pos_lmer_noi, model_life_sat_lmer_noi, type = "channels", analysis = "4. no imp")
dat_fig_results_channels_mim <- get_dat_res(model_aff_neg_lmer_mim, model_aff_pos_lmer_mim, model_life_sat_lmer_mim, type = "channels", analysis = "5. mult imp")

dat_fig_results_channels <- rbind(
  dat_fig_results_channels,
  dat_fig_results_channels_nco,
  dat_fig_results_channels_m,
  dat_fig_results_channels_noi,
  dat_fig_results_channels_mim
)
# make figure
fig_results_channels_comparison <- make_graph_res(
  dat_fig_results_channels,
  sesoi = "est"
  )
fig_results_channels_comparison

# safe figure
ggsave("figures/fig_results_channels_comparison.pdf", 
       width = 7, height = 4,
       plot = fig_results_channels)

Save results so that we can extract them in the manuscript.

save.image("data/workspace_2.RData")